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Benjamin Fredrick David, H.
- Survey on Crime Analysis and Prediction Using Data Mining Techniques
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Authors
Affiliations
1 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, IN
1 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, IN
Source
ICTACT Journal on Soft Computing, Vol 7, No 3 (2017), Pagination: 1459-1466Abstract
Data Mining is the procedure which includes evaluating and examining large pre-existing databases in order to generate new information which may be essential to the organization. The extraction of new information is predicted using the existing datasets. Many approaches for analysis and prediction in data mining had been performed. But, many few efforts has made in the criminology field. Many few have taken efforts for comparing the information all these approaches produce. The police stations and other similar criminal justice agencies hold many large databases of information which can be used to predict or analyze the criminal movements and criminal activity involvement in the society. The criminals can also be predicted based on the crime data. The main aim of this work is to perform a survey on the supervised learning and unsupervised learning techniques that has been applied towards criminal identification. This paper presents the survey on the Crime analysis and crime prediction using several Data Mining techniques.Keywords
Criminology, Crime Analysis, Crime Prediction, Data Mining.
References
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- Heart Disease Prediction using Data Mining Techniques
Abstract Views :184 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, IN
1 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, IN
Source
ICTACT Journal on Soft Computing, Vol 9, No 1 (2018), Pagination: 1817-1823Abstract
Data mining is a technique that is performed on large databases for extracting hidden patterns by using combinational strategy from statistical analysis, machine learning and database technology. Further, the medical data mining is an extremely important research field due to its importance in the development of various applications in flourishing healthcare domain. While summarizing the deaths occurring worldwide, the heart disease appears to be the leading cause. The identification of the possibility of heart disease in a person is complicated task for medical practitioners because it requires years of experience and intense medical tests to be conducted. In this work, three data mining classification algorithms like Random Forest, Decision Tree and Naïve Bayes are addressed and used to develop a prediction system in order to analyse and predict the possibility of heart disease. The main objective of this significant research work is to identify the best classification algorithm suitable for providing maximum accuracy when classification of normal and abnormal person is carried out. Thus prevention of the loss of lives at an earlier stage is possible. The experimental setup has been made for the evaluation of the performance of algorithms with the help of heart disease benchmark dataset retrieved from UCI machine learning repository. It is found that Random Forest algorithm performs best with 81% precision when compared to other algorithms for heart disease prediction.Keywords
Data Mining, Classification, Prediction, Heart Disease.References
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- Impact of Ensemble Learning Algorithms Towards Accurate Heart Disease Prediction
Abstract Views :217 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science, K.R. College of Arts and Science, IN
1 Department of Computer Science, K.R. College of Arts and Science, IN
Source
ICTACT Journal on Soft Computing, Vol 10, No 3 (2020), Pagination: 2084-2089Abstract
The medical field comprises of various techniques. Yet, the Data mining is playing a crucial role in determining the future of medications and patients’ state. This is because of the reliability offered by the various classification techniques. Still, accurate prediction of heart disease is becoming more and more challenging due to the influence of the various factors extracted from patients. Identifying these factors is a crucial research task. In such a scenario, the individual classification algorithms fail to produce perfect models capable of accurately predicting the heart disease. Hence, by introducing the ensemble learning methods, higher performance could be achieved leading to the accurate prediction of heart diseases. In this research work, the performance of the three ensemble classifiers namely Bagging, Stacking and AdaBoost is experimented and evaluated on various folds of cross validation with benchmark dataset for heart disease prediction. The base learners considered for constructing the ensemble are well known classifiers namely Support Vector Machine, Naive Bayes and K-Nearest Neighbour. The results illustrate the improved performance in terms of performance metrics and provide a better understanding of the accuracy, reliability and usefulness of the ensemble models in favouring improved performance for heart disease prediction.Keywords
Heart Disease, Patient Health, Prediction, Classification, Ensemble Classifier, Data Mining.References
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- Latha Parthiban and R. Subramanian, “Intelligent Heart Disease Prediction System using CANFIS and Genetic Algorithm”, International Journal of Biological, Biomedical and Medical Sciences, Vol. 3, No. 3, pp. 1-8, 2008.
- Nidhi Singh and Divakar Singh, “Performance Evaluation of K-Means and Hierarchal Clustering in Terms of Accuracy and Running Time”, Ph.D. Dissertation, Department of Computer Science and Engineering, Barkatullah University Institute of Technology, 2012.
- Sellappan Palaniappan and Rafiah Awang, “Intelligent Heart Disease Prediction System using Data Mining Techniques”, International Journal of Computer Science and Network Security, Vol. 8, No. 8, pp. 1-6, 2008.
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